| Reinforcement learning for personalization: a systematic literature review F den Hengst, EM Grua, A el Hassouni, M Hoogendoorn Journal of Data Science: Methods, Infrastructure, and Applications, 2020 | 41 | 2020 |
| Prediction using patient comparison vs. modeling: A case study for mortality prediction M Hoogendoorn, A El Hassouni, K Mok, M Ghassemi, P Szolovits 2016 38th Annual International Conference of the IEEE Engineering in …, 2016 | 39 | 2016 |
| Personalization of health interventions using cluster-based reinforcement learning A el Hassouni, M Hoogendoorn, M van Otterlo, E Barbaro International Conference on Principles and Practice of Multi-Agent Systems …, 2018 | 27 | 2018 |
| Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis L Roggeveen, A El Hassouni, J Ahrendt, T Guo, L Fleuren, P Thoral, ... Artificial Intelligence in Medicine 112, 102003, 2021 | 25 | 2021 |
| End-to-end Personalization of Digital Health Interventions using Raw Sensor Data with Deep Reinforcement Learning A El Hassouni, M Hoogendoorn, AE Eiben, M van Otterlo, V Muhonen 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 258-264, 2019 | 9 | 2019 |
| Using generative adversarial networks to develop a realistic human behavior simulator A el Hassouni, M Hoogendoorn, V Muhonen International Conference on Principles and Practice of Multi-Agent Systems …, 2018 | 6 | 2018 |
| Release of the systematic literature review into reinforcement learning for personalization F den Hengst, E Grua, A el Hassouni, M Hoogendoorn Zenodo, 2020 | 5 | 2020 |
| Mixing Consistent Deep Clustering D Lutscher, A Hassouni, M Stol, M Hoogendoorn International Conference on Machine Learning, Optimization, and Data Science …, 2021 | 1 | 2021 |
| pH-RL: A Personalization Architecture to Bring Reinforcement Learning to Health Practice A Hassouni, M Hoogendoorn, M Ciharova, A Kleiboer, K Amarti, ... arXiv preprint arXiv:2103.15908, 2021 | 1 | 2021 |
| A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions A el Hassouni, M Hoogendoorn, M van Otterlo, AE Eiben, V Muhonen, ... https://arxiv.org/abs/1804.03592, 2020 | 1 | 2020 |
| Data-driven models for mortality assessment at the Intensive Care Unit A el Hassouni, M Hoogendoorn | 1 | 2016 |
| Reinforcement learning for intensive care medicine: actionable clinical insights from novel approaches to reward shaping and off-policy model evaluation LF Roggeveen, A Hassouni, HJ de Grooth, ARJ Girbes, M Hoogendoorn, ... Intensive Care Medicine Experimental 12 (1), 1-13, 2024 | | 2024 |
| A model a day keeps the doctor away: Reinforcement learning for personalized healthcare A el Hassouni | | 2022 |
| Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse: the first 1633 critically ill COVID-19 … PJ Thoral, L Roggeveen, F van Diggelen, A el Hassouni, DR Guzman, ... Intensive care medicine 47 (4), 478-481, 2021 | | 2021 |
| Structural and Functional Representativity of GANs for Data Generation in Sequential Decision Making A el Hassouni, M Hoogendoorn, AE Eiben, V Muhonen International Conference on Machine Learning, Optimization, and Data Science …, 2020 | | 2020 |
| Generative Fourier-Based Auto-encoders: Preliminary Results A Zonta, A El Hassouni, DW Romero, JM Tomczak Machine Learning, Optimization, and Data Science: 6th International …, 2020 | | 2020 |
| Fraud detection using machine learning methods A el Hassouni Vrije Universiteit Amsterdam, 2016 | | 2016 |
| Automated Transaction Monitoring R Hageman, B Kostense, B van Walraven, HJ van der Wilk | | |
| VU Research Portal A el Hassouni, M Hoogendoorn | | |
